Method and System for Monitoring Core Body Movements

ABSTRACT

A system for monitoring core body movement comprises a sensor device for collecting a data set representing a plurality of core body movements over time from a monitoring device; a processor for determining a plurality of risk scores from the data set; and an output device for indicating the risk scores.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/622,671, filed Dec. 13, 2019, which is a U.S. National Stageapplication under 35 U.S.C. § 371 of International ApplicationPCT/AU2018/050600, filed Jun. 18, 2018, which claims the benefit ofpriority to Application AU 2017902302, filed Jun. 16, 2017. Benefit ofthe filing date of each of these prior applications is hereby claimed.Each of these prior applications is hereby incorporated by reference inits entirety.

FIELD OF THE INVENTION

The present invention relates to a method and system for monitoring corebody movements.

BACKGROUND

Physically demanding jobs expose workers to prolonged manual handlingactivities, such as lifting, holding or moving loads. These manualhandling activities can lead to negative health effects and manualhandling injuries, such as back pain. The effect of manual handlinginjuries caused through instantaneous injuries or by gradual wear andtear, has a significant social, human and organisational cost for bothworkers and workplace organisations.

Workplace organisations have duties to protect workers from the risk ofmanual handling injuries. These duties include identifying tasks thatinvolve hazardous manual handling, assessing the risk of these tasks,and reducing or eliminating the risks through risk control mechanisms.

Risk control mechanisms may be in the form of training programs foreducating workers about proper manual handling practices, trainingmanuals for referencing, and mechanical aids for reducing physicalefforts. However, the implementation of risk control mechanisms does notensure the elimination of manual handling injuries. Once implementationhas occurred, continuous monitoring of worker habits and adoption of therisk control mechanisms is necessary.

Safety officers are typically employed to monitor worker habits andtheir adoption of the risk control mechanisms. These safety officers arerequired to warn workers when they are exercising poor manual handlingpractices, provide re-education when workers revert to poor manualhandling practices, and report incidents to the organisation. However,safety officers may not be present or made aware of incidents whereworkers exhibit poor manual handling practices and/or incur a manualhandling injury.

The present invention seeks to overcome, or at least substantiallyameliorate, the disadvantages and shortcomings of the background art.

Any references to documents that are made in this specification are notintended to be an admission that the information contained in thosedocuments form part of the common general knowledge known to a personskilled in the field of the invention, unless explicitly stated as such.

In this specification the terms “comprising” or “comprises” are usedinclusively and not exclusively or exhaustively.

SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided a systemfor monitoring core body movement, comprising:

a sensor device for collecting a data set representing a plurality ofcore body movements over time from a monitoring device;a processor for determining a plurality of risk scores from the dataset; and an output device for indicating the risk scores.

In an embodiment the monitoring device is a single device used at a timefor the or each user and the collection of data is from the singlemonitoring device only.

In an embodiment of the invention, the data set representing bodymovements comprises a back bending angle and a torso twisting angle ofeach movement.

In an embodiment of the invention, the bending angle is measured by thechange in angle relative to horizontal in respect of a first referencepoint on a user's back. The horizontal is measured by the monitoringdevice and the refence point is determined from the position of themonitoring device on the user's torso.

In an embodiment of the invention, the twisting angle is measured by thechange in horizontal position of the first references point so as todetermine an angle relative to vertical relative to a second referencepoint on the user's back. In an embodiment, the vertical is taken tocoincide with a line of gravitational force.

In an embodiment of the invention, the data set representing bodymovements further comprises a static posture time of each movement.

In an embodiment of the invention, the static posture time is the timeduring which substantially no movement of the first reference point andthe second reference point on the user's back occurs.

In an embodiment of the invention, the processor for collecting the dataset is configured to add data to the data set each time a pre-set timeinterval has passed.

In an embodiment of the invention, the processor for determining theplurality of risk scores comprises a processor for determining a set ofrisk factor coefficients based on body movements.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by a combination of the bending angle and thetwisting angle.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by the static posture time. For example, seconds inan awkward posture.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by activity frequency. In an embodiment the activityis bending, twisting, or both.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by a duration of rest. For example, minutes beforenext activity.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by a duration of activity before rest. For example,hours of activity before a rest.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by the amount of exertion in an or each activity. Forexample, intensity of a lift.

In an embodiment of the invention, the risk factor coefficients comprisea frequency of activity in a given time. For example, number of liftsper minute.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by the amount of exertion in an or each activity.

In an embodiment of the invention, the risk factor coefficients comprisea value determined by one or more of the following: the amount of timetaken to bend down; the amount of time to bend up, the jerkiness of amovement, the angle of the torso at the end of the movement, thesmoothness of acceleration through the movement, the area under a curvemapping an angle of bending of a movement over time, and the sum ofacceleration of a movement over time.

In an embodiment of the invention, the system comprises a device forindicating a meaning of the risk score. In an embodiment when the riskscore reaches a threshold it is interpreted to mean that a movement isof high risk and the number of high risk movements is recorded. In anembodiment the device for indicating the risk scores comprises a devicefor indicating reaching of a threshold.

In an embodiment of the invention, the output indicating the risk scoresor the means reaching of a threshold comprises a visual indicator.

In an embodiment of the invention, the output indicating the risk scoresor the means reaching of a threshold comprises a tactile indicator.

In an embodiment of the invention, the output indicating the risk scoresor the means reaching of a threshold contains an audio indication.

In an embodiment of the invention, the system further comprisestransmitter for transmitting a signal.

In an embodiment of the invention, the signal comprises the data set.

In an embodiment of the invention, the signal comprises the plurality ofrisk scores or the meaning of the risk scores or the reaching of athreshold, or combinations of these.

In an embodiment the signal comprises the quantity of high riskmovements.

In an embodiment of the invention, the system further comprisesprocessor for determining a risk threshold of a user of the monitoringdevice using the plurality of risk scores and indicating to the user ifthe risk threshold has been reached.

In an embodiment of the invention, the system further comprises anoutput for indicating to the user that the risk threshold has beenreached. In an embodiment the system further comprises an output forcommunicating that the amount of high risk movements should be reduced.

In an embodiment of the invention, the processor for determining theplurality of risk scores is configured to analyse the data set todetermine a period of activity before rest.

In an embodiment of the invention, the processor for determining theplurality of risk scores is configured to analyse the data set todetermine the period of activity before rest is determined bydetermining the period over which one or more activities occur before aprolonged period without activity occurs.

In an embodiment of the invention, the processor for determining theplurality of risk scores is configured to analyse the data set todetermine a period of rest, wherein the period of rest comprises theduration of a period in which no activity occurs.

In an embodiment of the invention, the processor for determining theplurality of risk scores is configured to analyse the data set todetermine an amount of exertion.

In an embodiment of the invention, the processor for determining theplurality of risk scores is configured to determine the amount ofexertion by determining a period of time at which the user is bending ortwisting, or both. In an embodiment of the invention, the determinedamount of exertion is used to calculate an inferred weight being handledby the user.

In an embodiment of the invention, the risk scores are progressivelydetermined over time.

In an embodiment of the invention, the risk factor coefficients areprogressively determined over time, by cumulative analysis of the dataset over a working period, such as for example, over the working day.

According to a second aspect of the invention, there is provided asystem for monitoring core body movement, comprising:

a means for collecting a data set representing core body movements overtime from a monitoring device;a means for determining a plurality of risk scores from the data set;anda means for indicating the risk scores or indicating high riskmovements.

In an embodiment the means for collecting collects the data set so as tocomprise a plurality of core body movements over a period of time whichcomprises at least two movements before a rest.

According to a third aspect of the invention, there is provided a systemfor monitoring core body movement comprising;

means for collecting a data set representing a plurality of core bodymovements over time from a monitoring device;means for determining a plurality of risk scores based on the data set;means for determining when a risk score exceeds a risk threshold; andmeans for indicating to a user of the monitoring device when the riskthreshold has been reached.

In an embodiment, indicating to the user of the monitoring device whenthe risk threshold has been reached comprises indicating when a movementis high risk and/or indicating to the user the number of high riskmovements.

According to a fourth aspect of the invention, there is provided asystem for monitoring core body movement, comprising:

a sensor device for collecting a data set representing a plurality ofcore body movements over time from a monitoring device;a processor for determining a plurality of risk scores from the dataset; anda processor for determining when a risk score exceeds a risk threshold;andan output device for indicating to a user of the monitoring device whenthe risk threshold has been reached.

According to a fifth aspect of the invention, there is provided a systemfor monitoring core body movement comprising:

means for collecting a plurality of data sets representing a pluralityof core body movements over time from a plurality of monitoring devices;means for determining a plurality of risk scores for each monitoringdevice based on the plurality of data sets from each monitoring device;means for aggregating the plurality of risk scores from each monitoringdevice into a report; andmeans for indicating the report.

According to a sixth aspect of the invention, there is provided a systemfor monitoring core body movement comprising:

a sensor device for collecting a plurality of data sets representing aplurality of core body movements over time from a plurality ofmonitoring devices;a processor for determining a plurality of risk scores for eachmonitoring device based on the plurality of data sets from eachmonitoring device;a processor for aggregating the plurality of risk scores from eachmonitoring device into a report; andan output device for indicating the report.

According to a seventh aspect of the invention, there is provided amethod for monitoring core body movement, comprising:

collecting a data set representing a plurality of core body movementsover time from a monitoring device;determining a plurality of risk scores from the data set;determining a level of risk from the plurality of risk scores; andindicating the level of risk.

According to an eighth aspect of the invention, there is provided amonitoring device for monitoring core body movement, comprising:

at least one sensor positioned to sense a plurality of core bodymovements over time;a storage device configured to store the data set; anda communication device for transmitting the data set.

In an embodiment of the invention, the monitoring device furthercomprises an indicator for providing an indication of a level of risk.

According to a ninth aspect of the invention, there is provided acomputer program product, comprising a set of instructions forcontrolling a processor to:

-   -   retrieve a data set representing a plurality of core body        movements over time from a monitoring device;        determine a plurality of risk scores based on the data set;        determine a level of risk from the plurality of risk scores; and        indicate the level of risk.

According to a tenth aspect of the invention, there is provided acomputer program product, comprising a set of instructions forcontrolling a processor to:

-   -   retrieve a plurality of data sets representing a plurality of        core body movements over time from a plurality of monitoring        devices;        determine a plurality of risk scores for each monitoring device        based on the plurality of data sets from each monitoring device;        aggregate the plurality of risk scores from each monitoring        device into a report; and indicate the report.

According to an eleventh aspect of the invention, there is provided acomputer program product, comprising a set of instructions forcontrolling a processor to:

retrieve a data set representing a plurality of core body movements overtime from a monitoring device;determine a plurality of risk scores based on the data set;determine when a risk score exceeds a risk threshold; andtransmit instructions to the monitoring device to indicate to the userthat the risk threshold has been reached.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are described in the followingdetailed description by example only, with reference to the followingdrawings:

FIG. 1 illustrates an example of a system embodying the presentinvention;

FIG. 2A is a schematic view of an alternative clip-on type of monitoringdevice of the system of FIG. 1;

FIG. 2B is a block diagram of components of a monitoring device of thesystem of FIG. 1;

FIG. 3 is a block diagram of components of a remote computing resourceof the system of FIG. 1;

FIG. 4 is a block diagram of components of a personal computing resourceof the system of FIG. 1;

FIG. 5 is a block diagram of functional modules of the monitoring deviceof FIG. 2;

FIG. 6 is a block diagram of functional modules of the remote computingresource of FIG. 3;

FIG. 7 is a block diagram of functional modules of the personalcomputing resource of FIG. 4;

FIG. 8 is a flowchart illustrating a method of monitoring core bodymovement;

FIG. 9 is an example of a graphical representation of risk scoresproduced using the method of FIG. 8;

FIG. 10 is another example of a graphical representation of risk scoresproduced using the method of FIG. 8;

FIG. 11 is another example of a graphical representation of risk scoresproduced using the method of FIG. 8;

FIG. 12 is another example of a graphical representation of risk scoresproduced using the method of FIG. 8;

FIG. 13 is a graph of sample numbers against bending angle;

FIG. 14 is a graph of bending angles over time, with a first set (left)being of low intensity and a second set (right) be of high intensity;

FIG. 15 a graph of other bending angles over time, with a first one(left) being of high intensity and a second one (right) be of lowintensity

FIG. 16 is a heatmap of a distribution of target classes;

FIG. 17 is a graph of dependencies of weight class against initialrelative weight; and

FIG. 18 is a schematic diagram of the display of a report on a portablecomputing device.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

A worker may not be aware that they are exhibiting poor manual handlingpractices, such as a poor lifting posture, a high frequency of liftsduring a work shift, carrying a load for an extended period, not takinga rest after exertions, and not resting for long enough. For a worker toreduce the risk of injury, continuous monitoring of the core bodymovements can be conducted and a data set representing the movements canbe measured and stored for analysis. Additionally, the data set can beanalysed and a resultant report can be made available for the worker andan employment organisation to understand the worker's manual handlingpractices. Furthermore, the data set may be analysed to determinewhether a hazardous practice threshold has been reached, wherein theworker may be warned to cease or correct a dangerous manual handlingaction.

FIG. 1 illustrates a system 10 for monitoring the core body movements ofa user. The system 10 comprises a wearable item 12 that is worn by auser and which is able to communicate with a personal computing device14 that is able to connect to and communicate with a remote computingresource 16, and in an embodiment a remote computing device 18. Thewearable item 12 shown in FIG. 1 is a vest, however in an alternative,the wearable item is a clip-on tag that attaches to the user or theirclothing as depicted in FIG. 2A. The wearable item 12 comprises amonitoring device 22 for collecting a data set representing bodymovement of the user over time. The wearable item is for attaching themonitoring device 22 to the user and could take on other forms, such asfor example, a strap. Each of the computing devices 14, 18 can be asmartphone, a tablet computer, a laptop computer, a desktop computer orthe like. The remote computing resource 16 can be a network based(cloud) computer, a remote computer, or a remote server. The remotecomputing resource 16 can be implemented using any suitable computingdevice, preferably one with more processing power than the computingdevices 14, 18.

Referring to FIG. 2B, the monitoring device 22 comprises componentsincluding a processor 24, a storage device 26, a communication device 28and one or more sensors 30 for determining movement of the core body ofa user. The processor 24 may comprise a data storage device for storingworking data and operating instructions, and may be in the form ofnon-volatile solid state memory or similar. The storage device 26comprises a short term storage for storing data from the sensor 30and/or processed data from the processor 24, and may be in the form ofRAM, solid state memory or similar.

The processor 24 may comprise one or more physical, logical or virtualCPUs and is for executing the operating instructions, in the form of acomputer program, so as to control the components to operate as themonitoring device 22, as described further below. The instructions maybe in the form of firmware, or electronic circuitry, or embeddedsoftware, as appropriate and/or convenient. In a preferred form, thecomponents of the monitoring device 22 are low power drawing devicesthat are powered by a long life battery.

The processor 24 may be configured by operating instructions to operateas one or more of the functional modules described in relation to FIG.5. The functional modules may also, in addition or instead, and asappropriate and/or convenient, be formed of electronic circuitry. Thecommunication device 28 is typically a wireless network interface, suchas a Bluetooth transceiver. Other types of interface are possible, suchas IEEE 802.11.

The monitoring device 22 may also comprise a processor 32 for analysingthe data set from the sensor 30 to determine when an action or set ofactions reaches a safety threshold(s), in which case the user may bealerted. The processor 32 may make a similar analysis to that performedby the remote computing resource 16, as described below, or it may be asimplified version of the analysis. The processor 32 may be a separateprocessor to processor 24, in a physical, logical, or functional sense,or the processor 24 may be configured to operate as the processor 32.

Referring to FIG. 3, the remote computing resource 16 comprisescomponents including a processor 40, a storage device 42, and acommunication device 44. The processor 40 may comprise a data storagedevice for storing working data and operating instructions, and may bein the form of non-volatile solid state memory or similar. The storagedevice 42 comprises a long term storage for non-volatile storing of datareceived via the communication device 44 or processed data from theprocessor 40. The storage device 42 may be in the form of non-volatilesolid state memory, hard disk drive(s) or similar.

The processor 40 may comprise one or more physical, logical or virtualCPUs and is for executing the operating instructions, in the form of acomputer program, so as to control the components to operate as theremote computing resource 16, as described below. The instructions maybe in the form of firmware, or electronic circuitry, or software, asappropriate and/or convenient.

The processor 40 may be configured by operating instructions to operateas one or more of the functional modules described in relation to FIG.6. The functional modules may also, in addition or instead, asappropriate and/or convenient, be formed of electronic circuitry. Thecommunication device 44 is a computer network interface.

Referring to FIG. 4, the personal computing device 14 comprisescomponents including a processor 50, a storage device 52, acommunication device 54, a display 56 and a user input 58, such as atouch screen. The processor 50 may comprise a data storage device forstoring working data and operating instructions, and may be in the formof non-volatile solid state memory or similar. The storage device 52comprises a long term storage for non-volatile storing of data receivedvia the communication device 54 or processed data from the processor 50,and the storage device 42 may be in the form of non-volatile solid statememory or similar.

The processor 50 may comprise one or more physical, logical or virtualCPUs and is for executing the operating instructions, in the form of acomputer program, so as to control the components to operate as thepersonal computing device 14, as described below. The instructions maybe in the form of firmware, or electronic circuitry, or embedded and/orinstalled software, as appropriate and/or convenient.

The processor 50 may be configured by operating instructions of anapplication computer program to operate as one or more of the functionalmodules described in relation to FIG. 7. The functional modules mayalso, in addition or instead, as appropriate and/or convenient, beformed of electronic circuitry. The communication device 54 is typicallya cellular telephone network interface and/or a Wi-Fi interface and/or aBluetooth interface.

Referring to FIG. 5, the monitoring device 22 comprises functionalmodules including a sensor interface 62, a data storage interface 64, acommunication device interface 66, a sensor data processor 68, a remoteinput processor 70 and an output interface 72. The sensor interface 62takes a raw sensor input signal from the sensor 30 and provides it assensor data to other functional modules, such as the data storageinterface module 64 which stores the sensor data in the storage device26. The sensor data processor 68 is arranged to access sensor data fromthe sensor 30 via the sensor interface 62, and/or to retrieve data fromthe storage device 26 via the data storage interface 64. Data from theremote computing resource 16 may be received via the communicationdevice interface 66. This data may be stored in the storage device 26via the data storage interface 64. It may also be processed by theremote input processor 70. Data may be output to the personal computingdevice 14 (and then in turn to the remote computing resource 16) via anoutput interface 72.

Referring to FIG. 6, the remote computing resource 16 functional modulesinclude a communication device interface 82, a data storage interface84, a data processor 86, and a data analyser 88. The communicationdevice interface 82 is arranged to send and receive data to/from thepersonal computing device 14 or the remote computing device 18. The datastorage interface 84 stores the sensor data in the storage device 42.The data processor 86 is arranged to access stored data from datastorage device 42 via the data storage interface 84. Typically theprocessing involves determining the risk factors, as described below.The determined risk factors may then be stored in the data storagedevice 42 via the data storage interface 84. The data analyser 88 isarranged to access stored data from data storage device 42 via the datastorage interface 84, or the risk factors data from the data processor86 to analyse it. Typically the analysis involves determining whetherone or more of the risk factors reach a threshold, as described below.Risk factor data or threshold comparison data may be output to thepersonal computing device 14 (and then in turn to the monitoring device22) via a communication device interface 82.

Referring to FIG. 7, the personal computing device 14 comprisesfunctional modules including a communication device interface 92, a userinput interface 94, and a display interface 96. The communication deviceinterface 92 is arranged to send and receive data to/from the monitoringdevice 22 or the remote computing device 18. The user input interface 94receives inputs from the user. The display interface 96 providesinformation to the user.

The steps of the method of monitoring the core body movements of a usermay be implemented by the monitoring device 22, personal computingdevice 14 and remote computing resource 16 operating by, for example,their respective processors 24, 40 and 50 executing respectiveinstructions of a respective computer program so as to operate asdescribed further below.

In an embodiment, the measurement device collects a data setrepresenting body movement over time of a user. The personal computingdevice 14 relays the data set to the remote computing resource 16 whichanalyses the data set to determine a plurality of risk scores. The riskscores are collated to produce an indication of the level of risk forincurring injury in the manual handling activities in which the user hasbeen or is currently subjected to. The indication of the level of riskmay be the number of high risk movements in a given period. Theindication of the level of risk is transmitted to the personal computingdevice 14, which delivers the indication to the user. The personalcomputing device 14 may transmit the indication of the level of risk tothe wearable item 12 for it to deliver the indication to the user.

In a preferred embodiment, the indication is a visual indication. Thevisual indication may be in the form of a graph indicating the pluralityof risk scores plotted over a collection time frame of the data set. Inanother embodiment, the visual indication is in the form of a writtenreport which may include the visual graph. In a further embodiment, thevisual indication is in the form of lights which signify various risklevels. In an embodiment the visual indication may be the number of highrisk movements in a given period (eg per hour or per work shift).

In other embodiments of the invention, the indication may be an audioindication or a tactile indication. The audio indication is preferablyin the form of an aural report, providing information and alerts to theuser. In another embodiment, the audio indication is in the form of atone that may notify a user of various risk levels based upon volume andrepetitions. The tactile indication is preferably in the form of asingle vibration or a series of vibrations that may notify a user ofvarious risk levels based upon intensity and repetitions.

In an embodiment of the invention, the remote computing resource 16 is anetwork based (cloud) computer configured to determine whether a riskscore determined from the data sets has exceeded a risk threshold. Therisk threshold is a determined value that indicates when the user isparticipating in a manual handling activity that has the potential tocause serious injury. If the risk threshold has been exceeded, theremote computing resource 16 will generate an alert which is transmittedto the personal computing device 14 and to the wearable item 12. Thealert is in the form of a noticeable indication, such as an alarm tonefrom the speakers of the personal computing device 14 or vibrations fromthe monitoring device 22 of the wearable item 12.

In another embodiment of the invention, the system 10 comprises aplurality of wearable items 12 which collect a plurality of data setsrepresenting body movements over a period of time from a plurality ofusers. The plurality of data sets is transmitted to a plurality ofpersonal computing devices 14, which are each connected to the remotecomputing resource 16. The remote computing resource 16 is configured toreceive the plurality of data sets and analyses each data set separatelyto determine a plurality of risk scores for each data set. The pluralityof risk scores is collated to produce a plurality of indicators of thelevel of risk for incurring injury for each data set. The indicator oflevel of risk may be the number of high risk movements in a time period.Each indication of the level of risk is transmitted to each personalcomputing device 14 for delivery to each user. Each personal computingdevice 14 may transmit the indication to each wearable item for deliveryto each respective user.

The remote computing device 18 is configured to receive an aggregatedreport from the remote computing resource 16. In an embodiment theaggregated report is created by the remote computing resource 16 bycollating information gathered by the data sets and the plurality ofrisk scores.

FIG. 1 shows the wearable item 12 in the form of a vest 20, covering thetorso of the user, having a monitoring device 22. As noted above, apreferred alternative to the vest is a clip-on tag, as depicted in FIG.2A. The monitoring device 22 is integrated into the back of the vest 20with a patch. However, the monitoring device 22 may be integrated intothe vest 20 using any other conventional means and may be positioned atany suitable location on the torso. Additionally, the wearable item 12may be in the form of any wearable piece of clothing or a cover that isworn on the torso of the user. The monitoring device 22 may transmitmeasurement data to the computing device 14 by a direct connection, suchas a Bluetooth wireless connection, or other suitable connection, or itmay be relayed to the computing device 14 via a receiver device, such asa Bluetooth beacon. Notably the monitoring device 22 is in a singlepackage located on the torso. This is notably distinct from the two (ormore) monitoring devices at spaced apart locations on the body. Themonitoring device 22 may work in combination with another monitoringdevice, which might provide an enhancement to the monitoring describedherein, but the monitoring described herein does not require anothermonitoring device for sensing movement etc of the body.

In the present invention, the at least one sensor comprises a multi-axisaccelerometer, capable of detecting magnitude and direction ofacceleration in order to determine core body movements of the user, andpreferably including bending and twist of the back of the user. However,additional sensors such as gyroscopes, magnetometers, or furtheraccelerometers may be included in the monitoring device 22 as a sensorarray. In an embodiment the accelerometer is sampled by the sensorinterface 62 when a substantial change in acceleration is experienced bythe accelerometer. This can conserve battery life. In addition, orinstead, the accelerometer is sampled by the sensor interface 62 afterperiod of time has lapsed, such as for example 20 ms (producing asampling rate of 5 Hz). In a preferred form the sample rate is about1.25 Hz in sleep mode and 52 Hz in active mode.

As the at least one sensor 30 measures the movement of the core body,the processor 24 processes the measurements of core body movements. Inan embodiment the core movements are determined to be action and itsparameters, which for example include the maximum angle of bending andthe maximum angle of twisting in the action. It may also determine aduration of motion in the action, and one or more durations of stages ofmovement in the action. These are determined, collected and stored inthe storage device as the data set. In an embodiment, periodically thedata set can be transmitted, in the form of a signal, to an externalsource. The process will repeat according to a pre-set time interval.The time interval may be altered according to instructions from the useror from an external source, such as the remote computing resource 16.

The data set comprises values corresponding to specific core bodymovements. The primary core body movements are angular movements in avertical plane, and rotational movements. These movements are used todetermine a bending angle and a twisting angle of the user's torso,which provides information regarding trunk flexion/extension androtation. The bending angle is measured by the change in angle relativeto the horizontal of a first reference point, indicating the neutralstance of the user. The twisting angle is measured by the change inangle relative to a line of gravitational force of a second referencepoint, indicating the neutral stance of the user. The data set furthercomprises values relating to the static posture time. The static posturetime is the time during which substantially no movement of the first andsecond reference points occurs.

The monitoring device 22 is configured to transmit the data sets toexternal sources, such as the personal computing device 14. In thepresent invention, the monitoring device 22 will automatically transmitthe data sets when within range of the personal computer device 14.However, the transmission of the data sets may occur according tospecific instructions or prompts. In an example, the monitoring device22 may transmit the data sets to the personal computing device 14 uponreceiving a broadcast from an electronic beacon, such as a Bluetooth lowenergy beacon. In a further example, the monitoring device 22 maytransmit the data sets to the personal computing device 14 uponreceiving instructions from the personal computing device 14. In anotherembodiment, the monitoring device 22 may be configured to transmit thedata set to the remote computing resource 16.

In the current invention, the monitoring device 22 is configured toprovide means of indications to the user. The means of indications areused to indicate to the user the indications of the levels of riskdetermined by the remote computing resource 16, or the alert generatedby the remote computing resource 16. The means of indications may be inthe form of a speaker and/or a vibration device.

The monitoring device 22 may be ruggedized to suit the environment inwhich it will be operating.

The personal computing device 14 is preferably configured to remain incontinuous or frequent connection with the monitoring device 22, so toreceive the data sets from the wearable item 12. Alternatively, themonitoring device 22 will choose when to connect to the personalcomputing device 12. This may be periodically, or at the conclusion ofeach or a number of exertions. In another alternative, the personalcomputing device 14 will initiate connection to the monitoring devicewhen the user interacts with the device 14, whereupon stored data sincethe last download will be transmitted to the personal computing device14. In the present invention, the personal computing device 14 is usedas part of a relay network to transmit, in the form of a signal, thedata sets from the monitoring device 22 to the remote computing resource16 and instructions from the remote computing resource 16 to themonitoring device 22. The personal computing device 14 is furtherconfigured to receive the plurality of risk scores and associatedindications of levels of risk. The plurality of risk scores and theindications of levels of risk are stored on a storage device of thepersonal computing device 14 and may be displayed to the user forreference of their manual handling activities.

In an embodiment of the invention, the personal computing device 14 isfurther configured to receive the alert generated from the remotecomputing resource 16 for transmitting to the wearable item 12 and toindicate to the user of their participation in a manual handlingactivity that has the potential to cause severe injury. The indicationissued by the personal computing device 14 may be in the form of avisual alert, an audio alert, and/or tactile feedback.

In an embodiment of the invention, the personal computing device 14 isfurther configured to transmit location data and/or other identificationdata with the data set to the remote computing resource 16.

In the present invention, the remote computing resource 16 is configuredto receive and analyse the data set from the wearable item 12 todetermine a plurality of risk scores.

The analysis comprises assigning a plurality of risk factor coefficientsto the information found in the data sets. The risk factor coefficientsare comprised of values determined by the combination of the bendingangle/twisting angle, the static posture time, an amount of exertion, aperiod of exertion, a period of rest, and a period without rest. Anaction is defined as any collected values of significant magnitude fromthe first and second reference points in which the posture isnon-neutral. The amount of exertion is determined by the magnitude ofdeviance from the first and second reference points. The period of anaction is determined by the time frame in which exertion is determinedto have occurred. The period of rest is determined by the time periodbetween actions, or if actions are repeated frequently a prolongedperiod between when the last action occurred and when a new set ofactions is commenced. The period without rest is determined by the timeperiod between rests.

The risk factor coefficients are used as input into a calculation,resulting in a risk score representing the level of risk at a currentstate of time. The resultant plurality of risk scores is collated toprovide an indication of the level of risk of the current activities.Additionally, each risk score is compared to the risk threshold value,which will result in the generation of the alert. The remote computingresource 16 is further configured to transmit the plurality ofindications of the level of risk and the alerts to the wearable item 12and the personal computer device 14.

The remote computing resource 16 is further configured to receive aplurality of data sets from a plurality of wearable items 12 to analyseand determine a plurality of risk scores for the user of each wearableitem 12. The remote computing resource 16 will subsequently aggregatethe plurality of risk scores for each wearable item 12 to produce anaggregated report that is transmitted to the remote computer 18. Theremote computer 18 is typically the computer assigned to a safetyofficer or other workplace official that monitors workplace safety. Inthe present invention, the aggregated report provides a generalindication of the levels of risk associated to the workers from theworkplace organisation and/or from specific departments/sections of theworkplace organisation. However, the aggregated report may includedetailed information of individual users and their activities asrequired.

In an embodiment of the invention, the aggregated report may includeadditional information, such as location data and/or identification datato provide relevant information to the general indication of the levelsof risk. In an example, the aggregated report may combine location datawith the general indication of the levels of risk to determineworkplaces with higher occurrences of dangerous activities.

The remote computing resource 16 is controlled by a computer programexecutable by a computer of the remote computing resource 16 embodied ona computer readable media. The computer program comprises instructionsto configure the remote computing resource 16 as a special purposemachine that performs the functions previously described.

The personal computer device 14 is intended to be arranged as part ofthe system 10 which includes the monitoring device 22 and the remotecomputing resource 16. However, in an embodiment of the invention, thesystem may be comprised only of the monitoring device 22 and thepersonal computer device 14. In this embodiment, the personal computerdevice 14 is configured to perform the functions of the remote computingresource 16 in addition to its own functions.

The monitoring device 22 is intended to be arranged as part of thesystem 10, which includes the personal computer device 14 and the remotecomputing resource 16. However, in an embodiment of the invention, themonitoring device 22 may be configured to perform part of the functionsof the remote computing resource 16 in order to consistently generatethe alert of dangerous manual handling activities.

In an embodiment of the invention, the monitoring device 22 may beconfigured to perform the functions of the remote computing resource 16.

A method of operation 100 and use of the system 10 used to monitor thecore body movements of the user will now be described in more detailwith reference to FIG. 8.

The user wears the monitoring device 22 and conducts the typical manualhandling actions required for their job. The monitoring device 22 takesmeasurements 102. The raw measurements may be processed 104 into aconsistent format for a data set representing core body movements asmeasured by the monitoring device 22 over time. The data set is stored106 prior to being transmitted 108 to the personal computer device 14 ofthe user. The data set is stored on the personal computer device 14prior to transmission to the remote computing resource 16.

The steps above the line 130 are performed by the monitoring device 22.In an embodiment the steps below the line 130 are performed by theremote computing resource 16. However, these steps can be performed bythe monitoring device 22.

The data set is processed 110 by the remote computing resource 16 todetermine the plurality of risk factor coefficients. The risk scores arecalculated 112 using the plurality of risk factor coefficients. Theplurality of risk scores are collated into an indication of the level ofrisk of the manual labour activity prior to transmission 116 to thepersonal computer device 14 and/or the wearable item 12.

The generation of the alert of engaging in manual handling activity thatmay potentially lead to injury is initiated by analysis 120 of theplurality of risk scores, such as by comparison against thepredetermined risk threshold value by the remote computing resource 16.Alternatively, the number of high risk movements may be compared to athreshold, and if there are too many high risk movements in a givenperiod of time, then an alert is generated. If the risk score exceedsthe risk threshold value, the alert is generated 122 by the remotecomputing resource 16, which transmits the alert to the personalcomputer device 14 and/or the monitoring device 22. The alert warns theuser of their dangerous activity using indications such as visual, auraland/or tactile notifications.

The generation 118 of the aggregate report is initiated by the receiptof the plurality of data sets from the plurality of monitoring devices22 by the remote computing resource 16. The remote computing resource 16analyses the plurality of data sets to determine the plurality of riskfactor coefficients for each data set. The plurality of risk factorcoefficients is subsequently used as input for calculating the pluralityof risk scores for each data set. The plurality of risk scores isaggregated into the report providing general information relating toactivities of the users of the monitoring devices 22. The report issubsequently transmitted to the remote computer 18 of the safety officeror other official for review and the worker's individual report may betransmitted to their person computing device 14 for inspection of thereport as shown in FIG. 18.

In an embodiment, the plurality of risk scores is based on the riskfactor coefficients defined as bending coefficient (K_(B)), twistingcoefficient (K_(T)), bending/twisting coefficient (K_(BT)), continuousduration coefficient (K_(D)), force coefficient (K_(FO)), liftingfrequency coefficient (K_(FR)), rest coefficient (K_(R)), and staticposture coefficient (K_(S)).

K_(BT) derived from the bending and twisting angle, according to theformula:

K _(BT) =K _(B) +K _(T)

wherein K_(B) is the risk factor coefficient for a flexion bending angle(X), as derived below:

K _(B)=0.480383098521408×e ^(0.027460952380982X)

and K_(T) is the risk factor coefficient for the twisting angle (Z). Inan embodiment, the angle is a bending forward angle. A table reflectingthe Bending angle coefficient K_(B) is below:

TABLE 1 Coefficient (K_(B)) for bending angle (X) Bend angle <30 <45 <60<75 <90 105 Coefficient 1.09 1.65 2.50 3.77 5.69 8.59

The value for K_(T) is dependent upon the twist angle (Z) based on aformula below:

K _(T)=0.026*15.0*y*(y+1)/2+0.32*y,

where y is twisting angle divided by 15.

A table reflecting the Twisting angle coefficient K_(T) is shown below:

TABLE 2 Coefficient (K_(T)) for twisting angle (Z) Twist Angle <15 <30<45 <60 <75 90 Coefficient 0.71 1.81 3.30 5.18 7.45 10.11

In an example, Table 3 demonstrates a range of values for K_(BT) basedon various bending and twisting angles.

TABLE 3 Coefficient (K_(BT)) for various bending/twisting angles Z\\X 015 30 45 60 75 90 30 1.09 1.80 2.90 4.39 6.27 8.54 11.20 45 1.65 2.363.46 4.95 6.83 9.10 11.76 60 2.50 3.21 4.31 5.80 7.68 9.95 12.61 75 3.774.48 5.58 7.07 8.95 11.22 13.88 90 5.69 6.40 7.50 8.99 10.87 13.14 15.80105 8.59 9.30 10.40 11.89 13.77 16.04 18.70

For the evaluation of the risk being in an awkward static posture,static posture coefficient K_(ASP) was developed as below:

TABLE 4 Static posture time coefficients Time in static 10 sec 20 sec 30sec 40 sec 50 sec 60 sec Coefficient 1.45 1.55 1.65 1.75 1.85 1.95

To reflect difference in risk of high and low intensity movements,intensity coefficient K_(I) is defined according to whether theintensity is high or low. An example of high intensity is lift with theweight more than 30% of person body mass. The intensity coefficientK_(I) used is below:

TABLE 5 Intensity coefficients Intensity Low High Coefficient 1 8

In an embodiment there are further risk factor coefficients. Risk factorcoefficients K_(D), K_(FO), K_(FR), K_(R), and K_(S) are multipliers. Inan example, the values for K_(D), K_(FR), and K_(R) are shown below.

TABLE 6 Coefficient (K_(D)) for duration of continuous work Hourswithout recovery 0 1 2 3 4 5 6 7 8 Coefficient 1 1.05 1.12 1.2 1.33 1.481.7 1.85 1.99

TABLE 7 Coefficient (K_(FR)) for frequency of lifting Frequency(lifts/min) K_(FR) Multiplier < or =2 1.00 3 1.05 4 1.12 5 1.20 6 1.28 71.36 8 1.44 9 1.51 10 1.58 11 1.65 12 1.73 13 1.81 14 1.89 > or =15 1.97

TABLE 8 Coefficient (K_(R)) for duration of rest between work Rest time,minutes 5 10 15 20 30 45 60 Coefficient 0.93 0.85 0.78 0.73 0.68 0.610.54

In an embodiment, the plurality of risk scores is defined as generalrisk (R_(G)), bending risk (R_(B)), duration risk (R_(D)), force risk(R_(FO)), lifting frequency risk (R_(FR)), movement risk (R_(M)), staticrisk (R_(S)), and twisting risk (R_(T)). The plurality of risk scoresare used to determine the level of risk in the associated activity,wherein a higher value equates to higher risk.

In an embodiment, the general risk (RG) is derived from the followingformula:

R_(G) = R_(M) × R_(FR) × R_(R) wherein:$R_{M} = \frac{\left( {{K_{B{T{(1)}}} \times K_{S{(1)}}} + \ldots + {K_{B{T{(N)}}} \times K_{F{(N)}}}} \right)}{N}$$R_{FR} = \frac{\left( {{K_{B{T{(1)}}} \times K_{F{R{(1)}}}} + \ldots + {K_{B{T{(N)}}} \times K_{F{R{(N)}}}}} \right)}{N}$$R_{FR} = \frac{\left( {{K_{B{T{(1)}}} \times K_{R{(1)}}} + \ldots + {K_{B{T{(N)}}} \times K_{R{(N)}}}} \right)}{N}$

N=number of collected data.

In an embodiment, the bending risk (R_(B)), force risk (R_(FO)), staticrisk (R_(S)), and twisting risk (R_(T)) are the following formulae:

${R_{B} = \frac{\left( {K_{B{(1)}} + \ldots + K_{B{(N)}}} \right)}{N}}{R_{FO} = \frac{\left( {{K_{B{T{(1)}}} \times K_{F{O{(1)}}}} + \ldots + {K_{B{T{(N)}}} \times K_{F{O{(N)}}}}} \right)}{N}}{R_{S} = {R_{M} - R_{B} - R_{F} - R_{T}}}{R_{T} = {\frac{\left( {K_{B{T{(1)}}} + \ldots + K_{B{T{(N)}}}} \right)}{N} - R_{B}}}$

The risk coefficients K between 0 angle twisting and 15 degreestwisting, 15 degrees twisting and 30 degrees twisting etc., were countedas 0.026*z+0.32, where z was twisting angle.

In an alternative embodiment, movement risk R_(M) is calculated as sumof bending, twisting coefficient for corresponding angles multiplied bystatic coefficient and intensity coefficient, as below:

R _(M)=(K _(B) +K _(T))×K _(ASP) ×K _(I)

This risk score may be “averaged”, as above. The risk score may also becompared to a threshold to determine whether the movement is a “high”risk movement. For example, the threshold may be between 2 and 10,preferably around 2.5 to 5, more preferably about 3.5 to 4, and in anexample, 3.77.

Total risk R_(T) of a plurality of movements over a period of time iscalculated as sum of movement risks multiplied by frequency risk, lackof rest risk and recovery coefficient, as follows:

R _(T) =R _(M) ×K _(FR) ×K _(D) ×K _(R)

In an embodiment of the invention, K_(FO) is determined from aprediction of a weight class of a lift. The weight class prediction ismade based on a weight being lifted in the activity. Preferably theweight classes are divided into light, medium and heavy classes. In anembodiment ‘Light’ weight lifts have a coefficient multiplier valuesubstantially less than 1. In an embodiment ‘Medium’ weight lifts have acoefficient multiplier value of about 1. In an embodiment ‘Heavy’ weightlifts have a coefficient multiplier value substantially more than 1. Inan embodiment the risk factor coefficient K_(FO) is determined accordingto the table shown below.

TABLE 9 Coefficient (K_(FO)) for predicted weight class Predicted WeightClass K_(FO) Multiplier Light 0.5 Medium 1 Heavy 1.5

In an embodiment, the prediction of the weight class is made by usingthe recorded raw bending data, which may be a three component timeseries with a variable sample rate from the 3-axis accelerometer with atime stamp on each sample.

There is also a start time of the event and end time of the event.

Define ACC (acceleration) as an array of shapes (N, 4) defined by theraw data for a detected bending event. The format of ACC may be:[timestamp, aX, aY, aZ], where timestamp is a recorded time of a framein milliseconds and aX, aY, aZ are X, Y, Z MEMS-type 3-axisaccelerometer data in m/s².

From sub-series ACC, bending angle a is calculated by:

$a = {a\;\tan\; 2\left( {{aZ},{aY}} \right)*\frac{180}{\pi}}$

FIG. 13 shows a graph of sample number (x-axis) against bending angle(y-axis).

The bending angle changes over time, and can be divided into three partsof each bend: torso going down, torso almost in the static posture, andtorso going up. These durations are used, along with bending angle, andtwisting angle as a part of the set of signal features. The descendingportion of bending down and the ascending portion is bending up as shownin FIG. 13.

In an alternative intensity can be predicted based on measurement fromthe measuring device 22. Intensity during the lift is defined as amovement during which high exertion in the human back occurred. As anexample of such movement is lifting a heavy box or lifting while personhas a back pain. Any such movement is hard for the person and it isusually dangerous for the person's back.

To detect the difference between low and high-intensity movements andfurther classify them BIRCH clustering algorithm is used (seehttps://en.wikipedia.org/wiki/BIRCH). For this algorithm we prepareinput data which represents set of features extracted from each lift.Input data consists of five floating point numbers (to calculate thisnumbers we only using part of the lift when the person is rising(bending) up (angle value increasing over time):

-   -   1. Angle after lift—relative angle of the device after lift was        performed.    -   2. Area under curve—space under curve of angle, which represents        how steep or sloping was the change of lift angle.    -   3. Cumulative sum of each point of graph.    -   4. Graph smoothness—coefficient of how smooth the angle changes.    -   5. Average angular speed—represents how fast person was rising        up.

Before a classification can be started samples of lifts are collected.These are split into two groups, one for low intensity lifts and anotherone for high intensity lifts. Usually at least 100 samples of the liftsis enough to begin classifying.

Each of the five features is different for low-intensity andhigh-intensity lift but their absolute values are different for eachdifferent person. Unsupervised learning is used instead of supervisedlearning, which may need millions of samples for different categories ofpeople to have a good accuracy. By using unsupervised learning, we havean ability to have a unique intensity detection model for each personwhich will take in account all the personal differences.

Using five features we have a 5-dimensional space where each dimensionis feature value. This 5-dimensional space can be split by zones wherethe maximum concentration of lift movements occurred. These zones thencan be categorized into two categories, one for low-intensity movementsand another one for high-intensity.

On graphs in FIGS. 14 and 15, we can see the visual difference betweenlow-intensity and high-intensity movements: low-intensity are quicker,rising curve is more steep, smaller difference in start and stop angleafter lift. In both the low intensity lifts are to the left and the highintensity lifts are to the right.

Abstract features may also be used in some embodiments to assess therisk of a movement or series of movements, such as:

-   -   max(v), min(v), mean(v), median(v), where v is substituted by        each component of accelerometer data (aX, aY, aZ) and        accelerometer magnitude, where

accelerometer magnitude=√{square root over (aX ² +aY ² +aZ{circumflexover ( )}2)}−1; and

-   -   The first twenty coefficients of a Fast Fourier Transformation        (FFT) of accelerometer magnitude. In the case where the FFT        provides less than 20 coefficients, the missing coefficients are        filled in as zero.

The resulting feature list is:

-   -   1. Time of the body bending down    -   2. Time of the body rising up    -   3. Time of the body in near static posture    -   4. max(v), where v is bending angle    -   5. min(v), where v is bending angle    -   6. mean(v), where v is bending angle    -   7. median(v), where v is bending angle    -   8. max(v), where v is twisting angle    -   9. min(v), where v is twisting angle    -   10. mean(v), where v is twisting angle    -   11. median(v), where v is twisting angle    -   12. max(v), where v is aX    -   13. min(v), where v is aX    -   14. mean(v), where v is aX    -   15. median(v), where v is aX    -   16. max(v), where v is aY    -   17. min(v), where v is aY    -   18. mean(v), where v is aY    -   19. median(v), where v is aY    -   20. max(v), where v is aZ    -   21. min(v), where v is aZ    -   22. mean(v), where v is aZ    -   23. median(v), where v is aZ    -   24. max(v), where v is accelerometer magnitude    -   25. min(v), where v is accelerometer magnitude    -   26. mean(v), where v is accelerometer magnitude    -   27. median(v), where v is accelerometer magnitude    -   28.-48. 20 coefficients of FFT over accelerometer magnitude.

A model for predicting the relative object weight is determined from thesignal features, as input data, and the resulting features, as outputdata intended to be produced by the model. In an embodiment the model isa decision tree. A regression model is used to determine thecharacteristics of the decision tree from the input data and the outputdata. In an embodiment the regression model uses a gradient boostingmachine to realize the decision tree. The model may be refined overtime.

The relative object weight determined form the input data by the modelis then converted into a weight class. The weight class is in turn isused to determine K_(FO).

A training data set with target values of relative weight (mass of anobject divided by human mass) of an object which have been picked up orplaced to the ground is used by the regression model to determine/refinethe characteristics of the model.

To make uniform distribution targets the next sequence of transformationis performed:

y→(√{square root over (y)}*100) divided without remainder by 17, where yis a target array.

In order to determine the weight class, the relative object mass M_(r)is determined as follows:

${Mr} = \frac{Mo}{Mh}$

where M_(h) is human mass, and O_(m) is object mass.

In an embodiment a table of relative object mass to human weight isgiven in Table 7.

TABLE 10 Relative object mass based on Human mass and Object mass Humanmass, kg Relative object mass M_(r) 40 0.00 0.13 0.25 0.38 0.50 0.630.75 0.88 1.00 50 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 60 0.000.08 0.17 0.25 0.33 0.42 0.50 0.58 0.67 70 0.00 0.07 0.14 0.21 0.29 0.360.43 0.50 0.57 80 0.00 0.06 0.13 0.19 0.25 0.31 0.38 0.44 0.50 90 0.000.06 0.11 0.17 0.22 0.28 0.33 0.39 0.44 100 0.00 0.05 0.10 0.15 0.200.25 0.30 0.35 0.40 110 0.00 0.05 0.09 0.14 0.18 0.23 0.27 0.32 0.36 1200.00 0.04 0.08 0.13 0.17 0.21 0.25 0.29 0.33 130 0.00 0.04 0.08 0.120.15 0.19 0.23 0.27 0.31 Object mass 0 5 10 15 20 25 30 35 40 M_(o), kg

The classes can be allocated to the relative mass as follows:

${{Class}\mspace{14mu}\#} = {\left( {{100*\sqrt{\frac{Mo}{Mh}}} - {{mod}\left( {\left\lfloor {100*\sqrt{\frac{Mo}{Mh}}} \right\rfloor,17} \right)}} \right)/17}$

This provides a table (Table 11) of normalized classes that can bepredicted:

TABLE 11 Relative mass class based on Human mass and Object mass Humanmass, kg Class 40 1 3 3 4 5 5 6 6 6 50 1 2 3 4 4 5 5 5 6 60 1 2 3 3 4 45 5 5 70 1 2 3 3 4 4 4 5 5 80 1 2 3 3 3 4 4 4 5 90 1 2 2 3 3 4 4 4 4 1001 2 2 3 3 3 4 4 4 110 1 2 2 3 3 3 4 4 4 120 1 2 2 3 3 3 3 4 4 130 1 2 22 3 3 3 4 4 Object mass 0 5 10 15 20 25 30 35 40 M_(o), kg

FIG. 16 shows a distribution of target classes on a heatmap.

FIG. 17 shows dependencies of weight class with respect to initialrelative weight.

In an alternative embodiment instead of the two intensity classes notedabove, the present invention only uses classes #1, #2, #3, which may becorrelated to the ‘Light’, ‘Medium’ and ‘Heavy’ classes. Othercorrelations are possible.

Constants were defined with respect to the training set. The algorithmis validated and optimized by using cross-validation techniques.

FIGS. 9 to 12 provide graphical representations of example risk scoresproduced using the method and system of the present invention.

FIG. 9 shows a graph of a risk profile produced by manual labour overtime. The risk score is on the y-axis and time is on the x-axis. Therisk score is a composite of bending, twisting, static posture, force(exertion), frequency of exertion and lack of recovery. It can be seenthat up until about time unit 330 insufficient breaks were taken.However as successive breaks are taken there are significant decreasesin risk. The majority of the risk is in bending actions.

FIG. 10 shows a graph of a risk profile produced by manual labour overtime, with the axes and composition of the risk score being the same asthose of FIG. 9. It can be seen that up until about time unit 450 nosubstantial breaks were taken. However the valleys after this time pointshow that the breaks were insufficient to significantly reduce risk.

FIG. 11 shows a graph of a risk profile produced by manual labour overtime, with the axes and composition of the risk score being the same asthose of FIG. 9. It can be seen that there is a peak of risk at abouttime unit 300, but as corrective action is taken thereafter, such as byproviding a warning, the risk can be significantly reduced.

FIG. 12 shows a graph of a risk profile produced by manual labour overtime, with the axes and composition of the risk score being the same asthose of FIG. 9. It can be seen that the amount of bending risksignificantly increased at about time unit 100, with peak risks at abouttime units 200 and 600, but with the compositions of the risk at thesetimes being different.

In an embodiment the amount of force used during exertion is determinedby measuring the duration at which there is bending. In particular therate of change is measured. The rate of change may be a first orderderivative (speed), second order derivative (acceleration) or thirdorder derivative (jerk) measurement. The theory behind this is that whenstraining, that is applying a lot of force to an object, movement isminimal or slow until momentum in the change of position (angle) isachieved. For example, a change in acceleration can be used to infer theamount of force exerted and thus the weight or load being moved in anaction.

The reports provide the advantage of informing the safety officer orother official and the worker of the level of workplace safety and theadoption of risk control mechanisms. The report may also includerecommendations based upon the aggregated information.

The report and/or thresholds may be adjusted to each individual user, byfactoring in user specific parameters, such as exposure to vibration,side bending, force (Distance between the load and the body, weight ofthe load, speed of bending and coming up), fatigue, age, weight, height,BMI, and medical history.

In an alternative each movement is determined to be low or high risk ora series of movements have be determined to comprise a number of highrisk movement in a period of time, for example per hour or per workperiod (shift). This high risk movement or the number of high riskmovements over time can be communicated to the person working and/or ina safety report in a workforce.

Recommended remedial action can be determined based on the determinedrisk factors. For example, the following recommendation could be made tomitigate against the determined risk factors:

-   -   1. Rotation component can be eliminated or reduced. This will        reduce the total risk of low back pain;    -   2. Reduce duration of being in static posture;    -   3. Reduction of the angle of bending;    -   4. Take a 30 second rest break in the middle of the flexion;    -   5. Reduction of the frequency of lifting.

Reporting on movement risks over an extended period of time, rather thanor in addition to on individual movements can create postural awarenessof the workers. Reporting might be used to suggest kinesiotaping orbiomechanic education of the workers.

Modifications may be made to the present invention within the context ofthat described and shown in the drawings. Such modifications areintended to form part of the invention described in this specification.

What is claimed is:
 1. A system for monitoring core body movement,comprising: a sensor device for collecting a data set representing aplurality of separate core body movements being made over time; aprocessor for determining a plurality of risk scores from the data set,wherein determining each of the risk scores comprises determining a setof risk factor coefficients based on the body movements from the dataset, each risk coefficient representing a different type of riskassociated with body movements, and the risk coefficients beingmultiplied together in the determining of each of the plurality of riskscores; and an output device for indicating the risk scores.
 2. Thesystem according to claim 1, wherein the sensor device and the processorare in a single monitoring device for each user.
 3. The system accordingto claim 1, wherein the data set representing body movements comprises aback bending angle and a torso twisting angle of each movement and oneof the risk coefficients is based on back bending angle and torsotwisting angle.
 4. The system according to claim 1, wherein the data setrepresenting body movements further comprises a static posture time ofeach movement and one of the risk coefficients is based on the staticposture time, wherein the static posture time is the time at which theposture during the movement is static.
 5. The system according to claim1, wherein one of the risk factor coefficients is based on frequency ofactivity, wherein the frequency is how often a set of movements occurs.6. The system according to claim 1, wherein one of the risk factorcoefficients is based on a duration of rest between movements.
 7. Thesystem according to claim 1, wherein one of the risk factor coefficientsis based on a duration of activity before rest.
 8. The system accordingto claim 1, wherein one of the risk factor coefficients is based on anamount of predicted exertion in each movement.
 9. The system accordingto claim 1, wherein one of the risk factor coefficients is based on afrequency of movements in a given time before a rest occurs.
 10. Thesystem according to claim 1, wherein one of the risk factor coefficientsis based on a value determined by the amount of classified exertion ineach movement as a function of angle measurements over time.
 11. Thesystem according to claim 1, wherein the risk factor coefficientscomprise a value determined by one or more of the following: the amountof time taken to bend down; the amount of time to bend up, the jerkinessof a movement, the angle of the torso at the end of the movement, thesmoothness of acceleration through the movement, the area under a curvemapping an angle of bending of a movement over time, and the sum ofacceleration of a movement over time.
 12. The system according to claim1, wherein the processor for determining the plurality of risk scores isconfigured to determine the amount of exertion by determining a periodof time at which a user is bending and twisting.
 13. The systemaccording to claim 1, wherein the determined amount of exertion is usedto calculate an inferred weight being handled by a user.
 14. The systemaccording to claim 1, wherein the risk factor coefficients areprogressively determined over time, by cumulative analysis of the dataset over a working period.